FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
This addresses security vulnerabilities in federated learning for IoT applications, though it appears incremental as it builds on existing detection methods with blockchain integration.
The paper tackles the problem of data poisoning attacks in federated learning, which degrade model performance and integrity, by proposing FIDELIS, a blockchain-enabled framework that decentralizes the global server and uses a consensus-based judge model to detect poisoning, showing robustness and scalability in implementation.
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show \Sys is robust against data poisoning attacks and the creation of our judge model is scalable.